Data capture and analysis Books

Book SynopsisThe last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject.Data Analytics Applications in Gaming andTable of ContentsPart 1 – Introduction to game data mining. Part 2 – Data mining for games user research. Part 3 – Data mining for game technology.Part 4 – Visualization of large-scale game data.

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Book SynopsisData Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.Trade Review"This book is an essential guide to the promise and practice of data analytics and adaptive learning in higher education. These pioneers and practitioners share valuable insights all institutions can use to enhance learning and student success."—Diana G. Oblinger, Ph.D., President Emeritus, EDUCAUSE"Impacts on learning we called overdetermined can now be parsed but need an informed judgment rising to the complexities involved. Data Analytics and Adaptive Learning, a collection of the very best thinking about both, provides just that, rendering the (potentially) all-seeing and hyper-focused approaches of DA and AL fruitful, humane, transformative."—George Otte, former University Director of Academic Technology, The City University of New York"At last: a book by education experts about the use of digital Information and Communication Technologies (ICTs) not only for lowering the friction in data, but for processing information to help teachers and students. This can be the beginning of a more radical change in education."—Anders Norberg, Coordinator of the ERASMUS+ALBATTS Blueprint project for development of innovative education for the emerging European battery and electromobility value chain (ERASMUS is a European Union Initiative for Education Mobility and Development)"Digital learning is the new normal in higher education. The group of experts assembled in this book share important ideas and trends related to learning analytics and adaptive learning that will surely influence all of our digital learning environments in the future."—Charles R. Graham, Professor, Department of Instructional Psychology and Technology, Brigham Young University "The concept of personalized and adaptive learning has long been touted but seldom enacted in education at scale. Data Analytics and Adaptive Learning brings together a compelling set of experts that provide novel and research informed insights into contemporary education spaces."—Professor Shane Dawson, Executive Dean Education Futures, University of South Australia"Moskal, Dziuban, and Picciano challenge the reader to keep the student at the center and imagine how data analytics and adaptive learning can be mutually reinforcing in closing the gap between students from different demographics."—Susan Rundell Singer, Vice President for Academic Affairs and Provost, Rollins College, former Division Director for Undergraduate Education at the National Science Foundation"We are currently living in a digital age where higher education institutions have an abundance of accessible data. This book contains a series of chapters that provide insight and strategies for using data analytics and adaptive learning to support student success and satisfaction."—Dr. Norman Vaughan, Professor of Education, Mount Royal University, Calgary, Alberta, Canada"An important book that comes at a critical moment in higher education. We are swimming in an ocean of data and this book from some of the country’s top researchers and practitioners will help us make sense of it and put it in the service of student success." —Thomas Cavanagh, Ph.D. Vice Provost for Digital Learning, University of Central Florida"Data Analytics and Adaptive Learning is an excellent addition to the canon of literature in this field. The book offers several valuable perspectives and innovative ways of approaching both new and old problems to improve organizational outcomes."—Jeffrey S. Russell, P.E., Ph.D., Dist.M.ASCE, NAC, F.NSPE, Vice Provost for Lifelong Learning, Dean for Div. of Continuing Studies, University of Wisconsin-Madison"Data is used to customize experiences from buying an item to booking travel. What about learning—a uniquely human endeavor? This book contextualizes the complex answers to that question shedding light on areas with promise: learning analytics, adaptive learning and the use of big data."—Dale Whittaker, Senior Program Officer in Post-Secondary Success, Bill and Melinda Gates Foundation"Data Analytics and Adaptive Learning presents a timely and wide-ranging consideration of the progress of adaptive learning and analytics in levelling the educational playing field, while providing necessary cautions regarding the drawing of too many conclusions in what is still a nascent area."—Frank Claffey, Chief Product Officer, Realizeit Learning"Data Analytics and Adaptive Learning provides insights and best practices from leaders in digital learning who outline considerations for strategies, change management, and effective decision making related to data. As higher education expands its work in digital learning and utilizing data for decisions, this book is a must read!"—Dr. Connie Johnson, Chancellor, Colorado Technical University"Data analytics and adaptive learning compromise two of the most relevant educational challenges. This book provides excellent research approaches and analysis to answer practical questions related to digital education involving teachers and learners"—Josep M Duart & Elsa Rodriguez, Editor-in-Chief & Editorial Manager of the International Journal of Educational technology in Higher Education, the Universitat Oberta de Catalunya (UOC)"Data, analytics, and machine learning are impacting all jobs and industries. For education, the opportunities are immense, but so are the challenges. This book provides an essential view into the possibilities and pitfalls. If you want to use data to impact learners positively, this book is a must-read."—Colm Howlin, PhD, Chief Data Scientist, ReliaQuest"Data Analytics and Adaptive Learning helps the educational community broaden its understanding of these two technology-based opportunities to enhance education, looking at very different complementary contributions. Congratulations to the authors."—Alvaro Galvis, professor at University of Los Andes, Bogotá"The menus, dashboards, and pathways to effective data analytics and adaptive learning can be found in this massively timely and hugely impactful juggernaut."—Curtis J. Bonk, Professor of Instructional Systems Technology and adjunct in the School of Informatics, Indiana University Bloomington"Adaptive learning and learning analytics – should we use both or choose one? Do they imply organizational transformation? What works and what does not? In my opinion, the book is valuable reading for those seeking the answers to their questions."—Maria Zajac, Associate Professor (emeritus) at Pedagogical University Cracow and SGH Warsaw School of Economics, Certified Instructional Designer, Poland "Data analytics and adaptive learning platforms can direct support as needed to at-risk students, helping to create more equitable outcomes. This volume contains a timely collection of studies that examine the impact of these approaches."—John Kane, Director of the Center for Excellence in Learning and Teaching at SUNY Oswego."This book shines a spotlight on the potential for data analytics, adaptive learning and big data to transform higher education. The volume lights the way for those brave enough to embrace a new paradigm of teaching and learning that enacts a more equitable and person-centered experience for all learners."—Paige McDonald, Associate Professor and Vice Chair, Department of Clinical Research and Leadership, The George Washington School of Medicine and Health Sciences"Deftly weaving adaptive learning and analytic theory and practice together, the authors offer numerous examples of how these methods can help us address academic barriers to student success. Their work significantly strengthens the fabric of knowledge on how adaptive learning can benefit students (and faculty)." —Dale P. Johnson, Director of Digital Innovation, University Design Institute, Arizona State University"The authors of this book convince us that the concepts of data analytics and adaptive learning are tightly integrated, and the book provides insights on different aspects related to utilization of intelligent technologies and how to approach the learning cycle at different stages." —Eli Hustad, Professor in Information Systems, The University of Agder"Student success is a fundamental mission for all educational institutions. This book explores the current opportunities within analytics, adaptive Learning, and organizational transformation to generate wide-scale and equitable learning outcomes."—John Campbell, Associate Professor, Higher Education Administration, School of Education, West Virginia University"This book brings together top scholars making the connection between data analytics and adaptive learning, all while keeping pedagogical theory on the central stage. It’s a powerhouse driven in equal parts by excellence and innovation providing vision for educators on the quest for learner success across the spectrum."—Kimberly Arnold, Director of Learning Analytics Center of Excellence"Once again, a dream team of faculty, researchers, thought leaders and practitioners come up with this defining, must-read book for every institutional leader and teacher that is invested in the success of every student. This book based on years of research and practice gives the "how-to"."—Manoj Kulkarni, CEO at Realizeit Learning"The chapters in this book bring a desperately needed clarity and a depth of understanding to the topic of data and analytics, adaptive learning and learning more generally in higher education. You will leave this book smarter about these topics than you started and both you and higher education will be the beneficiary."—Glenda MorganTable of ContentsSection 1: Introduction 1. Data Analytics and Adaptive Learning: Increasing the Odds Section 2: Analytics 2. What We Want Versus What We Have: Transforming Teacher Performance Analytics to Personalize Professional Development 3. System-Wide Momentum 4. A Precise and Consistent Early Warning System for Identifying At-Risk Students 5. Predictive Analytics, Artificial Intelligence and the Impact of Delivering Personalized Supports to Students from Underserved Backgrounds 6. Predicting Student Success with Self-regulated Behaviors: A Seven-year Data Analytics Study on a Hong Kong University English Course 7. Back to Bloom: Why Theory Matters in Closing the Achievement Gap 8. The Metaphors We Learn By: Toward a Philosophy of Learning Analytics Section 3: Adaptive Learning 9. A Cross-Institutional Survey of the Instructor Use of Data Analytics in Adaptive Courses 10. Data Analytics in Adaptive Learning for Equitable Outcomes 11. Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry 12. 3-Year Experience with Adaptive Learning: Faculty and Student Perspectives 13. Analyzing Question Items with Limited Data 14. When Adaptivity and Universal Design for Learning are Not Enough: Bayesian Network Recommendations for Tutoring Section 4: Organizational Transformation 15. Sprint to 2027: Corporate Analytics in the Digital Age 16. Academic Digital Transformation: Focused on Data, Equity and Learning Science Section 5: Closing 17. Future Technological Trends and Research – Tony Picciano

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Book SynopsisData Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.Trade Review"This book is an essential guide to the promise and practice of data analytics and adaptive learning in higher education. These pioneers and practitioners share valuable insights all institutions can use to enhance learning and student success."—Diana G. Oblinger, Ph.D., President Emeritus, EDUCAUSE"Impacts on learning we called overdetermined can now be parsed but need an informed judgment rising to the complexities involved. Data Analytics and Adaptive Learning, a collection of the very best thinking about both, provides just that, rendering the (potentially) all-seeing and hyper-focused approaches of DA and AL fruitful, humane, transformative."—George Otte, former University Director of Academic Technology, The City University of New York"At last: a book by education experts about the use of digital Information and Communication Technologies (ICTs) not only for lowering the friction in data, but for processing information to help teachers and students. This can be the beginning of a more radical change in education."—Anders Norberg, Coordinator of the ERASMUS+ALBATTS Blueprint project for development of innovative education for the emerging European battery and electromobility value chain (ERASMUS is a European Union Initiative for Education Mobility and Development)"Digital learning is the new normal in higher education. The group of experts assembled in this book share important ideas and trends related to learning analytics and adaptive learning that will surely influence all of our digital learning environments in the future."—Charles R. Graham, Professor, Department of Instructional Psychology and Technology, Brigham Young University "The concept of personalized and adaptive learning has long been touted but seldom enacted in education at scale. Data Analytics and Adaptive Learning brings together a compelling set of experts that provide novel and research informed insights into contemporary education spaces."—Professor Shane Dawson, Executive Dean Education Futures, University of South Australia"Moskal, Dziuban, and Picciano challenge the reader to keep the student at the center and imagine how data analytics and adaptive learning can be mutually reinforcing in closing the gap between students from different demographics."—Susan Rundell Singer, Vice President for Academic Affairs and Provost, Rollins College, former Division Director for Undergraduate Education at the National Science Foundation"We are currently living in a digital age where higher education institutions have an abundance of accessible data. This book contains a series of chapters that provide insight and strategies for using data analytics and adaptive learning to support student success and satisfaction."—Dr. Norman Vaughan, Professor of Education, Mount Royal University, Calgary, Alberta, Canada"An important book that comes at a critical moment in higher education. We are swimming in an ocean of data and this book from some of the country’s top researchers and practitioners will help us make sense of it and put it in the service of student success." —Thomas Cavanagh, Ph.D. Vice Provost for Digital Learning, University of Central Florida"Data Analytics and Adaptive Learning is an excellent addition to the canon of literature in this field. The book offers several valuable perspectives and innovative ways of approaching both new and old problems to improve organizational outcomes."—Jeffrey S. Russell, P.E., Ph.D., Dist.M.ASCE, NAC, F.NSPE, Vice Provost for Lifelong Learning, Dean for Div. of Continuing Studies, University of Wisconsin-Madison"Data is used to customize experiences from buying an item to booking travel. What about learning—a uniquely human endeavor? This book contextualizes the complex answers to that question shedding light on areas with promise: learning analytics, adaptive learning and the use of big data."—Dale Whittaker, Senior Program Officer in Post-Secondary Success, Bill and Melinda Gates Foundation"Data Analytics and Adaptive Learning presents a timely and wide-ranging consideration of the progress of adaptive learning and analytics in levelling the educational playing field, while providing necessary cautions regarding the drawing of too many conclusions in what is still a nascent area."—Frank Claffey, Chief Product Officer, Realizeit Learning"Data Analytics and Adaptive Learning provides insights and best practices from leaders in digital learning who outline considerations for strategies, change management, and effective decision making related to data. As higher education expands its work in digital learning and utilizing data for decisions, this book is a must read!"—Dr. Connie Johnson, Chancellor, Colorado Technical University"Data analytics and adaptive learning compromise two of the most relevant educational challenges. This book provides excellent research approaches and analysis to answer practical questions related to digital education involving teachers and learners"—Josep M Duart & Elsa Rodriguez, Editor-in-Chief & Editorial Manager of the International Journal of Educational technology in Higher Education, the Universitat Oberta de Catalunya (UOC)"Data, analytics, and machine learning are impacting all jobs and industries. For education, the opportunities are immense, but so are the challenges. This book provides an essential view into the possibilities and pitfalls. If you want to use data to impact learners positively, this book is a must-read."—Colm Howlin, PhD, Chief Data Scientist, ReliaQuest"Data Analytics and Adaptive Learning helps the educational community broaden its understanding of these two technology-based opportunities to enhance education, looking at very different complementary contributions. Congratulations to the authors."—Alvaro Galvis, professor at University of Los Andes, Bogotá"The menus, dashboards, and pathways to effective data analytics and adaptive learning can be found in this massively timely and hugely impactful juggernaut."—Curtis J. Bonk, Professor of Instructional Systems Technology and adjunct in the School of Informatics, Indiana University Bloomington"Adaptive learning and learning analytics – should we use both or choose one? Do they imply organizational transformation? What works and what does not? In my opinion, the book is valuable reading for those seeking the answers to their questions."—Maria Zajac, Associate Professor (emeritus) at Pedagogical University Cracow and SGH Warsaw School of Economics, Certified Instructional Designer, Poland "Data analytics and adaptive learning platforms can direct support as needed to at-risk students, helping to create more equitable outcomes. This volume contains a timely collection of studies that examine the impact of these approaches."—John Kane, Director of the Center for Excellence in Learning and Teaching at SUNY Oswego."This book shines a spotlight on the potential for data analytics, adaptive learning and big data to transform higher education. The volume lights the way for those brave enough to embrace a new paradigm of teaching and learning that enacts a more equitable and person-centered experience for all learners."—Paige McDonald, Associate Professor and Vice Chair, Department of Clinical Research and Leadership, The George Washington School of Medicine and Health Sciences"Deftly weaving adaptive learning and analytic theory and practice together, the authors offer numerous examples of how these methods can help us address academic barriers to student success. Their work significantly strengthens the fabric of knowledge on how adaptive learning can benefit students (and faculty)." —Dale P. Johnson, Director of Digital Innovation, University Design Institute, Arizona State University"The authors of this book convince us that the concepts of data analytics and adaptive learning are tightly integrated, and the book provides insights on different aspects related to utilization of intelligent technologies and how to approach the learning cycle at different stages." —Eli Hustad, Professor in Information Systems, The University of Agder"Student success is a fundamental mission for all educational institutions. This book explores the current opportunities within analytics, adaptive Learning, and organizational transformation to generate wide-scale and equitable learning outcomes."—John Campbell, Associate Professor, Higher Education Administration, School of Education, West Virginia University"This book brings together top scholars making the connection between data analytics and adaptive learning, all while keeping pedagogical theory on the central stage. It’s a powerhouse driven in equal parts by excellence and innovation providing vision for educators on the quest for learner success across the spectrum."—Kimberly Arnold, Director of Learning Analytics Center of Excellence"Once again, a dream team of faculty, researchers, thought leaders and practitioners come up with this defining, must-read book for every institutional leader and teacher that is invested in the success of every student. This book based on years of research and practice gives the "how-to"."—Manoj Kulkarni, CEO at Realizeit Learning"The chapters in this book bring a desperately needed clarity and a depth of understanding to the topic of data and analytics, adaptive learning and learning more generally in higher education. You will leave this book smarter about these topics than you started and both you and higher education will be the beneficiary."—Glenda MorganTable of ContentsSection 1: Introduction 1. Data Analytics and Adaptive Learning: Increasing the Odds Section 2: Analytics 2. What We Want Versus What We Have: Transforming Teacher Performance Analytics to Personalize Professional Development 3. System-Wide Momentum 4. A Precise and Consistent Early Warning System for Identifying At-Risk Students 5. Predictive Analytics, Artificial Intelligence and the Impact of Delivering Personalized Supports to Students from Underserved Backgrounds 6. Predicting Student Success with Self-regulated Behaviors: A Seven-year Data Analytics Study on a Hong Kong University English Course 7. Back to Bloom: Why Theory Matters in Closing the Achievement Gap 8. The Metaphors We Learn By: Toward a Philosophy of Learning Analytics Section 3: Adaptive Learning 9. A Cross-Institutional Survey of the Instructor Use of Data Analytics in Adaptive Courses 10. Data Analytics in Adaptive Learning for Equitable Outcomes 11. Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry 12. 3-Year Experience with Adaptive Learning: Faculty and Student Perspectives 13. Analyzing Question Items with Limited Data 14. When Adaptivity and Universal Design for Learning are Not Enough: Bayesian Network Recommendations for Tutoring Section 4: Organizational Transformation 15. Sprint to 2027: Corporate Analytics in the Digital Age 16. Academic Digital Transformation: Focused on Data, Equity and Learning Science Section 5: Closing 17. Future Technological Trends and Research – Tony Picciano

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Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.

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Book SynopsisProvides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, datTable of ContentsForeword ix About the authors xi Preface xii Quotes about the book xv About the companion website xviii PART I THE INFORMATION QUALITY FRAMEWORK 1 1 Introduction to information quality 3 2 Quality of goal, data quality, and analysis quality 18 3 Dimensions of information quality and InfoQ assessment 31 4 InfoQ at the study design stage 53 5 InfoQ at the postdata collection stage 67 PART II APPLICATIONS OF InfoQ 79 6 Education 81 7 Customer surveys 109 8 Healthcare 134 9 Risk management 160 10 Official statistics 181 PART III IMPLEMENTING InfoQ 219 11 InfoQ and reproducible research 221 12 InfoQ in review processes of scientific publications 234 13 Integrating InfoQ into data science analytics programs, research methods courses, and more 252 14 InfoQ support with R 265 15 InfoQ support with Minitab 295 16 InfoQ support with JMP 324 Index 351

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